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Studies On Modified Quantum-behaved Particle Swarm Optimization Algorithm For Reactive Power Optimization In Power System

Posted on:2011-06-02Degree:MasterType:Thesis
Country:ChinaCandidate:K ZhengFull Text:PDF
GTID:2132360305961466Subject:Detection Technology and Automation
Abstract/Summary:PDF Full Text Request
Rational distribution of reactive power in power system is the prior condition which can ensure voltage quality and reduce the loss. Optimization adjustment of reactive power can act on secure and economical operation of power system. The distribution of reactive power not only determines the voltage profile, which is an important index of voltage quality, but also affects the security and economy of power systems. Reactive power optimization is an effective measure to assure the security, improve power quality and benefit of power systems. It is obvious that the investigation in reactive power optimization of power systems is of great significance. The objective of reactive power optimization is to find proper adjustments of control variables that would minimize system losses, maintain acceptable voltage levels and improve voltage stability throughout the system. The measure of Reactive power optimization mainly considers on-load tap changer, the optimal capacity of the capacitor, the voltage of generator under the steady load.Firstly, reducing active power loss is considered of the main object function in this paper, and the model of reactive optimization was established based on it. The penalty function is considered to deal with variables violating the constraints; In succession, several emerging intelligent optimization methods are further analyzed, based on the current research status of the reactive power optimization. Then the Quantum-behaved Particle Swarm Optimization (QPSO) which has better search efficiency and convergence property is selected to be modified. Modified Quantum-behaved Particle Swarm Optimization can effectively overcome the problem of premature convergence encountered by PSO and improve the optimization capability of it. Using the search policy with two swarms in modified QPSO can improve search precision and avoid premature convergence for the algorithm; And then, the project is used to deal with reactive power optimization problem, which based on modified PSO with nicer convergence property and search policy of two swarm-substituting. Aiming at the reactive power optimization, new solutions are suggested which are based on the modified Quantum-behaved Particle Swarm Optimization. Finally, in this paper, the modified algorithm is applied to the IEEE 30-bus system for a example to resolve the problem of reactive power optimization, and compares the result of PSO with inertia and QPSO. Through the comparison of its optimizing result with the other methods, the feasibility and validity of the modified Quantum-behaved Particle Swarm Optimization is validated at the aspect of reactive power optimization of the power system.
Keywords/Search Tags:Power system, Reactive power optimization, Quantum-behaved particle swarm optimization, Modified quantum-behaved particle swarm optimization, Two swarm-substituting
PDF Full Text Request
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